Detecting Ransomware Threats in Disk Storage through Behavioral Analysis using CNN2D and Flask Framework

DOI

10.2991/978-94-6463-471-6_48How to use a DOI?

Keywords

Ransomware; Encrypting; Machine learning; CNN2D; Flask

Abstract

A novel strategy for combatting ransomware has emerged, aiming to circumvent the limitations of traditional antivirus software which ransomware often evades. Ransomware, by encrypting files and restricting user access to systems and data, poses a significant threat. The proposed solution involves a ransomware detection system operating within virtual machines, which collects data on processor and disk I/O activities from the host machine. Utilizing a machine learning classifier, specifically a 2D Convolutional Neural Network (CNN2D), Voting Classifier and XGBoost. Its approach seeks to minimize overhead by collectively monitoring processes rather than individually, thereby reducing the risk of data corruption induced by ransomware. The system boasts rapid detection, particularly effective against both known and unknown ransomware variants, with the random forest classifier demonstrating superior performance. Moreover, the CNN2D architecture enhances feature extraction, allowing the model to identify relevant patterns for precise classification. By selectively monitoring processor and disk I/O events, the system maintains efficiency while ensuring comprehensive coverage against ransomware activities. Across diverse user loads and ransomware types, the system consistently achieves high detection rates. Detection outcomes are conveniently presented using the Flask Framework.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Bhasha Pydala
AU  - Allampati Sireesha
AU  - Peese Tejeswara Rao
AU  - Supriya Veluru
AU  - Ramireddy Sai Charan Reddy
AU  - V. Jyothsna
PY  - 2024
DA  - 2024/07/30
TI  - Detecting Ransomware Threats in Disk Storage through Behavioral Analysis using CNN2D and Flask Framework
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 496
EP  - 506
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_48
DO  - 10.2991/978-94-6463-471-6_48
ID  - Pydala2024
ER  -